Token Classification
Transformers
PyTorch
ONNX
Safetensors
xlm-roberta
fill-mask
punctuation-restoration
capitalization
truecasing
asr-post-processing
text-to-speech
tts-prosody
afrispeech
Instructions to use AfriSpeech/afri-punct-cap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AfriSpeech/afri-punct-cap with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AfriSpeech/afri-punct-cap")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AfriSpeech/afri-punct-cap") model = AutoModelForMaskedLM.from_pretrained("AfriSpeech/afri-punct-cap", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Welcome to the community
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